Workflow Orchestration

# Workflow Orchestration

Part of [Agent Skills™](https://github.com/itallstartedwithaidea/agent-skills) by [googleadsagent.ai™](https://googleadsagent.ai)

## Description

Workflow Orchestration provides a visual AI flow-building framework with MCP plugin support, conditional branching, parallel execution, and error recovery. The agent designs and implements multi-step workflows that chain AI operations, data transformations, and external service calls into reliable, repeatable automation pipelines.

Individual AI calls are useful; orchestrated workflows are transformative. A content pipeline that drafts text, fact-checks claims, generates images, formats for multiple platforms, and schedules publication—all triggered by a single input—replaces hours of manual coordination. This skill encodes the patterns for building such workflows: DAG-based execution, conditional routing, retry logic, and human-in-the-loop checkpoints.

The MCP (Model Context Protocol) plugin system extends workflows with external capabilities: database queries, file operations, API calls, and browser automation. Each MCP tool becomes a reusable node in the workflow graph, enabling agents to interact with any system that exposes an MCP interface. Workflows compose these nodes into complex automations without custom integration code.

## Use When

- Building multi-step AI automation pipelines
- Chaining LLM calls with data transformations and API integrations
- Implementing conditional logic in AI workflows (if/else, switch)
- Adding human-in-the-loop approval steps to automated processes
- Integrating MCP tools into repeatable workflows
- Orchestrating parallel AI tasks with dependency management

## How It Works

```mermaid
graph TD
    A[Trigger: Manual / Schedule / Webhook] --> B[Node 1: Input Parser]
    B --> C{Conditional Router}
    C -->|Type A| D[Node 2a: LLM Analysis]
    C -->|Type B| E[Node 2b: Database Query via MCP]
    D --> F[Node 3: Parallel Execution]
    E --> F
    F --> G[Node 3a: Image Generation]
    F --> H[Node 3b: Content Formatting]
    G --> I[Node 4: Human Review Checkpoint]
    H --> I
    I -->|Approved| J[Node 5: Publish via MCP]
    I -->|Rejected| K[Node 5: Return to Step 2]
    J --> L[Node 6: Log + Notify]
```

Workflows are directed acyclic graphs (DAGs) where each node is a discrete operation. The orchestrator manages execution order, passes data between nodes, handles retries on failure, and pauses at human checkpoints.

## Implementation

```typescript
interface WorkflowNode {
  id: string;
  type: "llm" | "mcp" | "transform" | "condition" | "human_review" | "parallel";
  config: Record<string, unknown>;
  next: string | string[] | ConditionalNext[];
  retries?: number;
  timeout_ms?: number;
}

interface ConditionalNext {
  condition: string;
  target: string;
}

interface Workflow {
  id: string;
  name: string;
  trigger: { type: "manual" | "schedule" | "webhook"; config: Record<string, unknown> };
  nodes: WorkflowNode[];
}

class WorkflowEngine {
  private state = new Map<string, unknown>();

  async execute(workflow: Workflow, input: unknown): Promise<unknown> {
    this.state.set("input", input);
    let currentNode = workflow.nodes[0];

    while (currentNode) {
      const result = await this.executeNode(currentNode);
      this.state.set(currentNode.id, result);

      const nextId = this.resolveNext(currentNode, result);
      currentNode = nextId ? workflow.nodes.find(n => n.id === nextId)! : undefined!;
    }

    return Object.fromEntries(this.state);
  }

  private async executeNode(node: WorkflowNode): Promise<unknown> {
    for (let attempt = 0; attempt <= (node.retries ?? 0); attempt++) {
      try {
        switch (node.type) {
          case "llm": return await this.executeLLM(node.config)